class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ProphetForecaster(changepoint_prior_scale=0.05, seasonality_mode='additive')
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
docstr: Predict using a trained forecaster.
  
  :param horizon: the number of steps forward to predict, the value defaults to 1.
  :param freq: the freqency of the predicted dataframe, defaulted to day("D"),
         the frequency can be anything from the pandas list of frequency strings here:
         https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
  :param ds_data: a dataframe that has 1 column 'ds' indicating date.
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code: def predict(self, horizon=1, freq="D", ds_data=None):
      """
      Predict using a trained forecaster.
  
      :param horizon: the number of steps forward to predict, the value defaults to 1.
      :param freq: the freqency of the predicted dataframe, defaulted to day("D"),
             the frequency can be anything from the pandas list of frequency strings here:
             https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
      :param ds_data: a dataframe that has 1 column 'ds' indicating date.
      """
      if self.internal.model is None:
          raise RuntimeError(
              "You must call fit or restore first before calling predict!"
          )
      return self.internal.predict(
          horizon=horizon, freq=freq, ds_data=ds_data
      )
  
file_path: analytics-zoo/pyzoo/zoo/chronos/forecaster/prophet_forecaster.py
imports: from zoo.chronos.forecaster.abstract import Forecaster
  from zoo.chronos.model.prophet import ProphetModel
label_desc: Use image node_id 4 for calling the ProphetForecaster obj's underlying member method code with example usage: obj.predict(horizon, freq, ds_data) and returns: self